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dc.contributorDepartment of Applied Mathematicsen_US
dc.creatorXu, Ben_US
dc.creatorCai, Jen_US
dc.creatorHe, Den_US
dc.creatorChowell, Gen_US
dc.creatorXu, Ben_US
dc.date.accessioned2023-05-10T02:00:19Z-
dc.date.available2023-05-10T02:00:19Z-
dc.identifier.issn0022-5193en_US
dc.identifier.urihttp://hdl.handle.net/10397/98559-
dc.language.isoenen_US
dc.publisherAcademic Pressen_US
dc.rights© 2019 Elsevier Ltd. All rights reserved.en_US
dc.rights© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.rightsThe following publication Xu, B., Cai, J., He, D., Chowell, G., & Xu, B. (2020). Mechanistic modelling of multiple waves in an influenza epidemic or pandemic. Journal of theoretical biology, 486, 110070 is available at https://doi.org/10.1016/j.jtbi.2019.110070.en_US
dc.subjectInfluenza outbreaken_US
dc.subjectMechanistic modelen_US
dc.subjectMultiple wavesen_US
dc.subjectNumber of infection wavesen_US
dc.subjectModelling frameworken_US
dc.titleMechanistic modelling of multiple waves in an influenza epidemic or pandemicen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume486en_US
dc.identifier.doi10.1016/j.jtbi.2019.110070en_US
dcterms.abstractMultiple-wave outbreaks have been documented for influenza pandemics particularly in the temperate zone, and occasionally for seasonal influenza epidemics in the tropical zone. The mechanisms shaping multiple-wave influenza outbreaks are diverse but are yet to be summarized in a systematic fashion. For this purpose, we described 12 distinct mechanistic models, among which five models were proposed for the first time, that support two waves of infection in a single influenza season, and classified them into five categories according to heterogeneities in host, pathogen, space, time and their combinations, respectively. To quantify the number of infection waves, we proposed three metrics that provide robust and intuitive results for real epidemics. Further, we performed sensitivity analyses on key parameters in each model and found that reducing the basic reproduction number or the transmission rate, limiting the addition of susceptible people who are to get the primary infection to infected areas, and limiting the probability of replenishment of people who are to be reinfected in the short term, could decrease the number of infection waves and clinical attack rate. Finally, we introduced a modelling framework to infer the mechanisms driving two-wave outbreaks. A better understanding of two-wave mechanisms could guide public health authorities to develop and implement preparedness plans and deploy control strategies.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of theoretical biology, 7 Feb. 2020, v. 486, 110070en_US
dcterms.isPartOfJournal of theoretical biologyen_US
dcterms.issued2020-02-07-
dc.identifier.scopus2-s2.0-85074901469-
dc.identifier.pmid31697940-
dc.identifier.eissn1095-8541en_US
dc.identifier.artn110070en_US
dc.description.validate202305 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberAMA-0202-
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS20441345-
dc.description.oaCategoryGreen (AAM)en_US
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